





import argparse


lowerCAmelCase_        =							'''docs/source/_static/js/custom.js'''






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		Union[str, Any]:






				"""simple docstring"""


				with open(_UpperCamelCase					,							encoding='''utf-8'''					,							newline='''\n'''					) as f:
								snake_case_		:       List[str]           =    f.readlines()
				snake_case_		:       str           =    0

				# First let's put the right version
				while not lines[index].startswith('''const stableVersion ='''					):
								index += 1
				snake_case_		:       str           =    f'''const stableVersion = "v{version}"\n'''

				# Then update the dictionary
				while not lines[index].startswith('''const versionMapping = {'''					):
								index += 1

				# We go until the end
				while not lines[index].startswith('''}'''					):
								index += 1
				# We add the new version at the end
				lines[index - 1] += f'''    "v{version}": "v{version}",\n'''

				with open(_UpperCamelCase					,							'''w'''					,							encoding='''utf-8'''					,							newline='''\n'''					) as f:
								f.writelines(_UpperCamelCase					)


if __name__ == "__main__":
	lowerCAmelCase_        =							argparse.ArgumentParser()
	parser.add_argument('''--version''', help='''Release version.''')
	lowerCAmelCase_        =							parser.parse_args()
	update_custom_js(args.version)






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_time_series_transformer": [
        "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TimeSeriesTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TimeSeriesTransformerForPrediction",
      "TimeSeriesTransformerModel",
      "TimeSeriesTransformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



